It is difficult for marketers to quantify whether digital marketing spend is driving desired user actions for their business (e.g., product purchases). The reason is because a subset of every marketer's audience that is exposed to the marketer's content (e.g., an advertisement) would possibly convert on the marketer's desired action without the need for the marketing exposure in the first place. To quantify the incremental contribution that digital marketing has for a marketer's business, marketers have traditionally performed studies, often referred to as media effectiveness studies, true-lift analyses, or view-through analyses. These studies are used to determine a percentage of users in a test group who performed a conversion (e.g., purchased a product) compared to a percentage of users in a control group who also performed the conversion. The users are segmented into the test group and control groups based on what content is delivered to each user. Users in the test group receive the marketer's content, while users in the control group receive some content unrelated to the marketer's business. Segmenting users in this way to quantify incremental contribution of digital marketing takes a lot of time, resources, expertise, analysis, and purposefully wasted media spend just to arrive at a conclusion that is flawed and far too binary to be applied to the complicated nature of human reaction to the billions of different permutations of marketing exposure and the various impacts that those permutations may have on a consumer.